144 results about "Constant false alarm rate" patented technology

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

The invention relates to the technical field of radar signal processing, and discloses a complex ground reflection environment-oriented dual-path target detection method, which comprises the following steps of: performing quantile truncation and discretization processing on original range profile data, mapping a processed signal to a D-channel discrete feature space and generating a D-dimensional discrete feature matrix; the D-dimensional discrete feature matrix is input into a dual-channel parallel processing architecture, a CFAR adaptive detection module is adopted in a first path to suppress strong clutters to generate a first response matrix, and a false attention mechanism is adopted in a second path to enhance weak targets to generate a second response matrix; performing spatial alignment and weighting processing on the first response matrix and the second response matrix, and performing cross-domain confidence fusion to output a comprehensive response matrix; and carrying out fixed threshold binarization processing on the comprehensive response matrix based on a global threshold to obtain a target detection result. The method has both local gain compensation and global adaptive suppression, and can realize more robust detection of a weak target under a complex background.

A non-contact real-time monitoring system of physiological signs based on millimeter-wave radar includes a millimeter-wave radar and multiple modules for processing radar signals. The millimeter-wave radar is configured to continuously transmit electromagnetic wave signals and simultaneously receive echo signals, perform frequency mixing processing on the echo signals to obtain an intermediate frequency signal, and process the intermediate frequency signal to obtain a radar four-dimensional data matrix. Human body physiological signs are monitored by analyzing body thoracic cavity micro-motion information in signals through the modules; a target echo is processed by adopting a constant false alarm rate detection algorithm, and invalid signals are filtered. A self-adaptive range cell selection algorithm based on short-time stability of respiratory signals is adopted to capture radar echoes reflecting physiological movement. Mixed human body physiological sign signals are processed by using a VMD algorithm, and key parameters in VMD are optimized by using a GWO algorithm.

The invention discloses a clustering algorithm for Terahertz radar extended target tracking. The clustering algorithm comprises three steps of drawing an azimuth distance map by using original data, carrying out two-dimensional unit average constant false alarm rate detection on an azimuth distance dimension, and clustering by a connected domain labeling method. In the first step, a grid interpolation method is used for carrying out zero interpolation on the reflection intensity of discrete points, so that subsequent calculation of background noise is facilitated; in the second step, a threshold value is calculated by setting related parameters, and part of clutter interference is filtered out; in the third step, adjacent data points are communicated through an eight-field method, then morphological closed operation is designed to merge adjacent targets, the problem of excessive segmentation is effectively avoided, then a minimum area threshold value is added to filter clutter interference, effective targets are reserved, and finally the clustering effect is more remarkable through target center point labeling. The clustering algorithm for Terahertz radar extended target tracking is low in cost, high in processing speed and remarkable in effect, and has certain application value in the fields of Terahertz radar extended target tracking and the like.

The invention relates to the technical field of urban low-altitude safety monitoring, and discloses a low-altitude economic airspace passive safety early warning method and system. The method comprises the following steps: collecting multi-modal observation data streams of a target at different moments; generating multi-modal frame data with a unified timestamp; establishing a spatial confidence distribution map; extracting fusion discrimination features; executing multi-channel small target detection; continuously tracking the preliminary target; and introducing a constant false alarm rate framework to set a dynamic confidence threshold to execute alarm judgment. Compared with the prior art, the technical problem that high-confidence all-weather passive monitoring cannot be realized in urban core areas with dense buildings, serious signal shielding and easy GPS failure due to dependence on single vision or radar information in the prior art is solved. According to the method, the space confidence distribution diagram and the dynamic constant false alarm mechanism based on the shielding diagram are constructed by fusing the multi-modal sensing data, so that the safety early warning of the low-altitude small aircraft is realized, and the passive safety guarantee capability of the low-altitude economic airspace is improved.

The invention discloses an unmanned aerial vehicle spectrum anomaly detection and trajectory generation method based on unsupervised learning, and the method comprises the steps: continuously collecting broadband radio frequency signals through an SDR, and obtaining an IQ data stream; performing short-time Fourier transform to obtain a time-frequency spectrogram; performing robust normalization processing on the time-frequency spectrogram to obtain a standardized spectrogram after slow-varying drift suppression; performing constant false alarm rate detection, and generating a primary abnormal candidate binary mask; identifying spectrogram sub-blocks corresponding to the primary abnormal candidate binary masks from the standardized spectrogram, and inputting the spectrogram sub-blocks into a pre-trained unsupervised deep learning model to obtain an abnormal scoring graph; fusing with the primary abnormal candidate binary mask to generate an abnormal heat map; and clustering the abnormal heat map in a time-frequency dimension, carrying out cross-time window trajectory association and filtering on a cluster, and outputting a time-frequency trajectory of the unmanned aerial vehicle signal. According to the invention, the reliability of anomaly detection is enhanced, the risk of false detection and missing detection is reduced, and the continuity and accuracy of track generation are guaranteed.

The invention discloses an active sonar target identification method based on message passing multi-hypothesis tracking, and belongs to the technical field of underwater target detection. The method comprises the following steps: firstly, processing single-cycle sonar data by using adaptive constant false alarm detection, obtaining observation points, and forming an effective observation set through hierarchical clustering; thirdly, extrapolating the existing target, calculating the association probability of the existing target and the current observation by adopting a message passing method, and generating a plurality of groups of association hypotheses; then, the target state is corrected through Kalman filtering, target rejection and new management are executed, and a high-probability hypothesis is reserved; and finally, estimating the motion speed based on the target multi-period position sequence, and distinguishing a static object from a suspicious motion target according to a speed threshold. Through the multi-hypothesis tracking and probability message passing mechanism, clutter interference and static target influence in a complex underwater environment are effectively overcome, and the stability of target tracking, the accuracy of speed estimation and the overall reliability of system identification are remarkably improved.

The invention relates to the technical field of intelligent sensors, in particular to an unmanned aerial vehicle track intelligent tracking method based on a millimeter-wave radar, which comprises the following steps: receiving echo signals collected by the front end of the millimeter-wave radar, carrying out constant false alarm rate detection and Doppler frequency shift extraction on the echo signals, generating an unmanned aerial vehicle target space coordinate set, and carrying out target tracking on the unmanned aerial vehicle. And mapping the target space coordinate set of the unmanned aerial vehicle to a three-dimensional Euclidean space. According to the method, a non-convex optimization objective function containing beam switching time cost and precision constraint is constructed, and mathematical optimal solution of radar beam resources is realized. The generated adaptive scheduling instruction is used for adjusting the pulse repetition frequency and the beam residence time, it is ensured that radar front-end parameters dynamically evolve along with the target state, overall centroid tracking is emphasized for rigid body formation so as to save resources, independent high-frequency refreshing is emphasized for discrete targets so as to ensure the precision, and the accuracy is improved. And the continuity and robustness of trajectory tracking of the unmanned aerial vehicle in a complex electromagnetic environment are improved.

The present disclosure provides a constant false alarm rate-based downhole casing nondestructive testing method, device and medium. The method can include: obtaining a detection threshold according to a preset false alarm probability and background noise power of a target pulsed eddy current array detection signal, wherein the false alarm probability refers to the probability that the detection system incorrectly determines the presence of a damaged target when a non-damaged target is present in the downhole casing; screening the target pulsed eddy current array detection signal based on the detection threshold to obtain a downhole casing damage detection result signal; and determining the position of the downhole casing damage according to the downhole casing damage detection result signal. Through this technical solution, the detection threshold can be dynamically adjusted, and the position of the downhole casing damage can be accurately located, thereby improving the efficiency and accuracy of downhole casing damage detection.

The invention discloses an anti-high dynamic Doppler low signal-to-noise ratio satellite signal blind detection method, which comprises the following steps of: performing frequency domain analysis on a delay correlation signal according to a signal symbol rate, and dividing a signal band and a noise band near the delay correlation signal to construct a detection quantity to perform constant false alarm rate (CFAR) detection; and finally, obtaining a final signal existence or disappearance result through time consistency judgment. According to the method disclosed by the invention, the analytic CFAR threshold and the state machine smoothing are combined, and the problem of detection failure in a low signal-to-noise ratio and high dynamic Doppler environment in the prior art is solved.

The invention relates to a multichannel real-time processing method and device for ultrahigh frequency river detection radar signals. The method comprises the following steps: synchronously sampling analog echo data of multiple channels to obtain a digital echo signal; based on the digital echo data, pulse compression processing is carried out, and baseband multi-channel data is output; performing memory scheduling processing on the baseband multichannel data based on a ping-pong buffer strategy to obtain continuous data blocks; based on a preset adaptive filtering weight, performing partition adaptive weighted summation filtering on the continuous data blocks to generate a clutter suppressed single-channel data matrix; performing constant false alarm rate detection based on the single-channel data matrix to obtain a target detection list; and based on the target detection list, performing visual rendering, and generating a radar detection report of the river detection radar for detection. By adopting the method, clutters can be effectively suppressed, and the credibility of the river detection radar signal is improved.

The application provides a hypersonic platform radar target detection method, device and electronic equipment, relates to the technical field of hypersonic radar target detection, and the method comprises the following steps: inputting a received first echo signal into a time domain echo signal model, converting the first echo signal from a first signal domain to a second signal domain by using the time domain echo signal model, and obtaining a second echo signal; filtering the second echo signal by using an optimal parameter filter, obtaining a filtered third echo signal, converting the third echo signal into a third signal domain, and obtaining a coherent accumulation result; determining whether a target is detected by using a constant false alarm rate detection based on the coherent accumulation result, and determining the distance of the target based on the position of a peak value in the accumulation result in the case that it is determined that the target is detected. The hypersonic platform radar target detection method, device and electronic equipment provided by the application can realize high-precision, high-efficiency and highly adaptable radar target detection.

The invention discloses a sea surface small target detection method and system based on a magnetic KNN and an optimized concave packet, and belongs to the technical field of radar signal processing. Comprising the following steps: extracting high-dimensional features of sea clutters and echoes to be detected, and selecting three-dimensional feature points of the clutters and the echoes to be detected according to a dimension reduction algorithm; processing the three-dimensional feature points of the clutters by adopting a constant false alarm algorithm to obtain clutter feature points after constant false alarm; on the basis of the clutter feature points after the constant false alarm, constructing a concave packet by adopting an optimized concave packet construction algorithm; the clutter feature points in the concave packet serve as vertexes of a tetrahedron, and the concave packet is divided into tetrahedral grids; positioning the points in the tetrahedrons by using a pointLocation function, judging the indexes of the tetrahedrons where the points are located, and if the indexes exist, judging that the points are clutters; and if the index does not exist, judging as a target. According to the detection method, the construction efficiency of the concave packet is also improved while the relatively high detection probability is ensured, and the method has relatively good generalization.

The present application relates to the field of signal processing, deep learning and radar target detection, and particularly relates to a radar target detection method based on shallow and deep feature fusion. The present application solves the problem of poor detection generalization caused by relying on shallow features in the prior art by fusing shallow multi-domain features and deep learning features. The shallow features are subjected to multi-domain analysis, weighting and normalization processing, enhancing the ability to distinguish target echoes and sea clutter. The deep features are extracted by 1D-ResNet50 and spliced with the shallow features, combining physical meaning and expression ability, and improving the feature representation ability of the model. Combined with the constant false alarm rate algorithm, the detection threshold is dynamically adjusted to ensure stability and robustness in complex marine environments. The present application significantly improves the accuracy, generalization and practicality of target detection, and has high engineering application value.

The invention belongs to the technical field of radar signal processing, and particularly relates to a self-adaptive constant false alarm radar target detection method and system based on grouping and sequencing, and the method comprises the steps: extracting signal values of a to-be-detected unit and a reference unit thereof through receiving a radar echo signal; and according to the environmental background characteristics, adaptively selecting a processing strategy of direct sorting without grouping or sorting after averaging by grouping, and after an ordered sequence is generated, dynamically selecting the kth statistical magnitude to calculate a detection threshold. And respectively adopting different parameter adaptive mechanisms for the uniform background and the multi-target background: determining a k value by maximizing an expected value of a detection target number under the uniform background, and dynamically adjusting the k value and a grouping number n according to an interference target number under the multi-target background. According to the method, grouping processing and adaptive parameter selection are innovatively combined, the problem that the detection performance of a traditional CFAR algorithm is reduced in a complex environment is effectively solved, and the detection probability in a multi-target scene can be remarkably improved while the constant false alarm rate is kept.

The invention relates to a radar target detection and tracking technology, and provides a sky wave over-the-horizon radar particle filter track-before-detect method in order to solve the limitation of the traditional particle filter under long-time accumulation, a virtual frame and a speed interval are divided through motion constraint to obtain a plurality of parallel processing channels, so that a coherent pulse number estimation interval is reduced, and the estimation precision is improved. And coherent processing is carried out. Meanwhile, particles are randomly generated only in a distance-Doppler unit exceeding a threshold through a unit average constant false alarm rate CA-CFAR method before particle filtering processing, and the number of particles is further reduced. According to the method, the operand is reduced, and the weak target detection and tracking capability of the OTH radar is improved. According to the method, echo data preprocessing and coherent and non-coherent processing technologies are combined, and the calculation complexity is reduced while the detection and tracking performance of the weak target under the sky wave over-the-horizon radar is improved by means based on multiple channels and a low threshold.

The application provides a terrain awareness and warning system excessive descent rate warning constant false alarm rate detection method, comprising the following steps: step 1: calculating the no-alarm trajectory data of an airplane according to the flight parameters of the airplane; calculating the post-alarm trajectory data of the airplane; performing collision detection on the no-alarm flight trajectory and the post-alarm flight trajectory with a first-order Markov random terrain to determine whether a successful alarm or an unnecessary alarm is generated; step 2: using a Monte Carlo method to perform a large number of simulations, counting the successful alarm rate and the false alarm rate to generate an SOC curve, and selecting an optimal alarm threshold point; step 3: selecting a constant false alarm rate range according to the obtained optimal alarm threshold point; obtaining the relationship between the descent rate and the alarm height from the selected constant false alarm rate; and step 4: repeating step 3 to obtain an optimal constant false alarm rate. The application can effectively reduce the false alarm rate of the excessive descent rate mode alarm, increase the safe flight probability, and adapt to more flight conditions.

The invention relates to the technical field of millimeter wave radars, and discloses a millimeter wave signal reconstruction method and system for indoor monitoring and a storage medium. The method comprises the following steps: acquiring video frame data of a target object and original I / Q data acquired by a millimeter wave radar, extracting key feature data of the target object from the video frame data, and mapping the key feature data to a radar coordinate system to form human body prior information; preprocessing the original I / Q data to obtain an amplitude matrix and a phase matrix, performing normalization processing on the amplitude matrix based on a noise baseline, and combining with the phase matrix to generate radar echo data; establishing a prior drive mapping model based on the two modes, and performing enhancement processing on the radar echo data to obtain enhanced radar echo data; and a binary detection map is generated by applying constant false alarm rate detection, the target object is tracked by adopting Kalman filtering, the motion trail information of the target object is obtained, and the accuracy of indoor area target object detection is improved.

The invention provides a millimeter-wave radar cross-environment perception generalization method and system based on course learning, and relates to the technical field of millimeter-wave radar perception, and the method comprises the steps: carrying out the constant false alarm rate detection of a distance velocity spectrogram obtained through preprocessing, and carrying out the interception to obtain a most easy sample data set; carrying out course learning stage progressive learning on the most likely sample data set to obtain a training stage total number and a training stage sample range of a millimeter wave radar cross-environment perception generalization model constructed by introducing a self-adaptive feature map down-sampling mechanism, and training the millimeter wave radar cross-environment perception generalization model; obtaining a trained millimeter wave radar cross-environment perception generalization model; and inputting to-be-identified millimeter-wave radar data into the millimeter-wave radar cross-environment perception generalization model, and outputting an environment perception identification result of the to-be-identified millimeter-wave radar data. According to the method, millimeter wave radar perception has cross-environment generalization capability, and the identification precision in an unknown scene is improved.

This application proposes a radar signal processing method, apparatus, electronic device, and storage medium, belonging to the field of radar information processing technology. The method includes: acquiring channel data and optimizing the channel data; using a first filter to suppress clutter in the optimized channel data to display moving targets; using a second filter to further filter out clutter from the clutter-suppressed channel data to obtain target detection data; and detecting the target detection data based on a constant false alarm rate to obtain radar-detected targets. The apparatus includes: a data acquisition module, a data optimization module, a first filtering module, a second filtering module, and a target detection module. This application achieves hardware and software decoupling, significantly improving the real-time performance of the radar signal processing system, while also improving anti-interference capability and signal-to-noise ratio.

The application provides a radar target detection method, system, device and medium based on two-feature fusion, and belongs to the technical field of radar signal processing. The method comprises the following steps: constructing training data by using existing radar target detection data, extracting phase linearity based on a training data extraction process, and extracting the relative peak height of the Doppler spectrum by using a relative peak height extraction process; based on the phase linearity and the relative peak height, a linear dimension reduction method with compact distribution is used to obtain a fusion matrix of two features; reference distance unit data and to-be-detected distance unit data are obtained in real time, the phase linearity extraction process and the relative peak height extraction process are respectively performed, and the fusion matrix is used to generate fusion features; based on the fusion features, the parameters of a generalized extreme value distribution are estimated, a detection statistic is constructed, a preset false alarm probability is combined to calculate an adaptive threshold, the target is judged by comparing the detection statistic with the threshold, and the constant false alarm rate detection under clutter suppression is realized.

This invention relates to a method and apparatus for resolving velocity ambiguity based on constant frequency assistance. The method includes the following steps: a radar sequentially transmits a constant frequency band signal and a linear continuously modulated pulse train, and acquires the radar echo signal; both the constant frequency band echo signal and the sawtooth band echo signal are mixed to obtain a constant frequency band intermediate frequency signal and a sawtooth band intermediate frequency signal; the constant frequency band intermediate frequency signal is processed by a fast Fourier transform to obtain a target true velocity estimate; the sawtooth band intermediate frequency signal is sequentially processed by a two-dimensional fast Fourier transform, a two-dimensional constant false alarm rate (CFAR) processing, and a centroid condensation processing to obtain a target range velocity estimate; based on a velocity matching method using a Fourier rotation factor, the target true velocity estimate is matched with the target range velocity estimate to obtain the true velocity of all targets. This method not only resolves velocity ambiguity but also improves velocity accuracy, offering advantages such as high velocity measurement accuracy, high real-time performance, and simplicity.

A method and apparatus for radar sensing in a disaggregated radio access network. The apparatus includes interface circuitry to receive data from a radio unit and processing circuitry to perform radar sensing processing on the received data to determine at least one of range, velocity, or direction of at least one target in an environment. The radar sensing processing includes range processing, Doppler processing, two-dimensional range-Doppler periodogram processing, constant false alarm rate (CFAR) detection processing, angular resolution processing, and post processing. The data received from the radio unit is beam domain data obtained by receive beamforming, and the processing circuitry is configured to perform decompression processing for converting the beam domain data to antenna domain data and perform at least the angular resolution processing and the post processing on the antenna domain data. The processing circuitry may perform the decompression processing after the CFAR detection processing.

This invention discloses a Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on a large-scale selection logic, belonging to the field of image detection technology. The steps of this invention include: first, setting a detection window, which includes a detection unit, a reference window composed of a leading edge window and a trailing edge window, and a protection region; then, using the detection window to traverse the SAR ship remote sensing image pixel by pixel, with each movement distance equal to the width of the detection unit; subsequently, calculating the mean ratio (MR) of the leading edge and trailing edge windows, and using a threshold to determine whether the clutter is uniform or non-uniform; then, selecting a reference window based on the determination result; for uniform clutter, selecting the entire reference window, and for non-uniform clutter, selecting the leading edge / trailing edge window with the larger mean and widening it by 1; finally, calculating the detection statistic. R Set detection threshold T ,according to R and T The relationship between the detection unit and the target is determined. This invention maintains an approximately constant false alarm rate in non-uniform backgrounds at clutter edges, significantly outperforming the excessive increase in false alarms observed with traditional Wilcoxon technology, thus greatly improving false alarm control capabilities.

The invention discloses a low-altitude dynamic fence monitoring method and system based on a train positioning protection level, which are used for solving the problem that a static fence is difficult to adapt to high-speed movement and positioning errors of a train. According to the system, through a vehicle-mounted high-completeness positioning unit, the train position and the protection level thereof are solved in real time by using an integrity monitoring algorithm, and the position uncertainty is quantified; the space-time dynamic ellipsoid electronic fence with the train as the center is constructed by combining the train speed, and the size of the space-time dynamic ellipsoid electronic fence stretches and retracts in real time along with the protection level and And the ground monitoring terminal maps the fence into a space-time mask, adaptively adjusts the constant false alarm rate threshold value of the radar and the focal length of the camera, expands the field of view and reduces the detection threshold when the positioning uncertainty is large, focuses the core area when the positioning is accurate, and realizes hierarchical response and accurate coverage. According to the invention, satellite positioning completeness parameters are introduced, so that a safety blind area caused by positioning drift is effectively eliminated, and the accuracy and timeliness of low-altitude intrusion monitoring along the railway are improved.

This invention discloses a constant false alarm rate (CFAR) detection system and method for small sea surface targets based on feature game theory, relating to the field of radar signal processing technology. The system and method provided by this invention can be used for small sea surface target detection. It includes: extracting potential high-dimensional nonlinear features from the radar received signal using a feature extractor module; transforming the target detection problem into a binary classification problem using a target detector module to obtain the probability prediction result of the target detection task; and using the output feature vector of the feature extractor module and the probability density function of the target detector module as the input of a sea state discriminator module; and training the sea state discriminator module based on feature game learning to obtain the label distribution for different sea state environments. Therefore, using the above method, the detection of small sea surface targets under time-varying sea states is achieved, improving the detection rate and reducing the false alarm rate, while also exhibiting strong robustness.

The invention provides a medium-high repetition frequency velocity measurement method and system based on a height-finding radar, and relates to the technical field of data processing and signal processing. According to the method, the target point track distance obtained through data processing is used as prior information, the number of target distance folding times is calculated, and the problem of range finding ambiguity caused by medium-high repetition frequency is solved; a medium-high repetition frequency mode is adopted for tracking speed measurement, and the problem that the low repetition frequency speed measurement range is small is solved; a multi-frequency mode is adopted, the prior information of the track speed of a target point is utilized, and corresponding pulse repetition frequency and emission frequency are calculated and selected, so that the blind speed problem is solved; the interference problem of ground clutter and meteorological clutter is solved by adopting multi-dimensional CFAR (Constant False Alarm Rate) processing and a method of narrowing a target search range through acquired priori information such as target point track distance and point track speed; a point track velocity measurement and Doppler velocity measurement combined processing method is adopted to solve the problem of velocity measurement resolution of monopulse radar.

This invention discloses a method and system for monitoring the breathing and heartbeat of multiple individuals based on FMCW radar, relating to the fields of next-generation information technology and smart healthcare. The key technical points of this invention include: combining a constant false alarm rate (CFAR) detection method with the maximum number of extreme points within the range-angle matrix to identify the number and location of targets within the radar monitoring area, extracting their phase information, then using a sparse optimization method to extract the breathing and heartbeat signals of the targets from the phase signals, and finally displaying the target's location and breathing / heartbeat information in real time on the system interface. This invention overcomes the limitation of previous methods that could only detect a single person, effectively monitoring the breathing and heartbeat information of multiple individuals, and the system is easy to operate, demonstrating strong innovation and competitiveness.

The application discloses a kind of unmanned aerial vehicle spectrum anomaly detection and trajectory generation method based on unsupervised learning, including by SDR continuous acquisition broadband radio frequency signal, obtain IQ data stream;Short-time Fourier transform is carried out, and time-frequency spectrogram is obtained;Robust normalization processing is carried out to time-frequency spectrogram, and after inhibiting slow change drift, standardization spectrogram is obtained;Constant false alarm rate detection is executed, and preliminary anomaly candidate binary mask is generated;From standardization spectrogram, the spectrogram sub-block corresponding to preliminary anomaly candidate binary mask is identified, input into pre-trained unsupervised deep learning model, and abnormal score chart is obtained;With preliminary anomaly candidate binary mask is fused, and generates abnormal heat map;Abnormal heat map is clustered in time-frequency dimension, and trajectory correlation and filtering are carried out to clustering cluster across time window, and the time-frequency trajectory of unmanned aerial vehicle signal is output.The application enhances the reliability of anomaly detection, reduces the risk of false detection and missed detection, and guarantees the continuity and accuracy of trajectory generation.

The application discloses a kind of multi-target detection method and system based on millimeter wave radar, comprising: obtaining the original echo data collected by millimeter wave radar, the original echo data includes the target data point of multiple targets;Select a preset number of data points from the target data point of the multiple targets, and the selected data point is used as effective data point;Based on the effective data point, the multiple targets are detected.The application uses non-strict threshold screening constant false alarm rate algorithm combined with double index determination method and spatial distance judgment algorithm, realizes multi-target detection while reducing the influence of multipath effect on result data, improves the accuracy of target point detection, greatly reduces the output data volume of frequency domain data processing, reduces the operation amount of logic processing, which is beneficial to the implementation of the algorithm on edge mobile device.